Counting objects using datasets containing multiple annotations per image remains a fairly unexplored problem. In ch:counting-with-uncertainty, we proposed a two-stage methodology for counting biological structures in microscopy images under weak-labeled conditions. However, the proposed solution still has some limitations that we believe should be tackled in the future. Specifically, the two stages presented are implemented by two separate models, increasing the computational cost of the solution. In fact, although the scorer model is small compared to the localization model, formulating a unique model, still trained in two separate stages, could deliver the same counting performance while sharing parameters among stages and decreasing the overall computation needed. In light of our results, the detection-based and the segmentation-based models, whose scores exhibited a fair correlation with the raters' agreement in general, are the most promising solution to be extended for integrating the rescoring stage. Additional improvements can be made in scenarios where severe occlusions and overlaps between objects to be counted occur. Along this path, an interesting research direction concerns the extension of density-based methods to model also the objectness or detection confidence of objects. This could increase the counting performance in crowded scenarios while permitting confidence-based filtering of objects. Finally, another line of research could be to apply similar strategies also in the crowd counting task. Indeed, as outlined by [1]}, also the datasets belonging to this field suffer from mistaken ground-truth labels since there are cases where annotations are not done on parts of the images as it is virtually impossible to distinguish heads of neighboring people; a small set of images labeled by multiple annotators could be helpful, by fully exploiting the redundancy of the information represented by the different raters' opinions.
